Simulating Tissues with Oneiric
Patrick Martin
Introduction
Benchmarking spatial analysis tools is tricky especially when there exists few ground truth data sets. Instead, we decided that we would use simulated data since this allows us to have a strong ground truth. We can also control the parameters used to create the data sets in order to simulate a variety of potential biological situations.
We wrapped our simulation in an R package - Oneiric - to faciliate to reproducibility and replicability of our work.
library(oneiric)
library(scater)
library(ggplot2)
library(RColorBrewer)
set.seed(288)Data
Oneiric provides multiples territory structures - one of them being based on a tinkerbell progression chaos map. We provide a set of parameters that will produce useable chaos maps.
It is also possible to search for other parameters using the
find_tinkerbell function.
Chaos Parameters
data(oneiric)
map_params## a b c d x_0 y_0
## [1,] -1.4524975 1.2802377 -0.89194171 2.4186510 0.45164818 -0.11938045
## [2,] -0.9833781 0.7158569 -2.22051348 0.5401524 0.15201547 0.70978265
## [3,] 1.5359136 -0.5865262 0.46922684 1.9351586 -0.80089519 -0.25490827
## [4,] -0.9097808 -0.8748624 2.27694361 0.4794840 0.29575882 -0.73100054
## [5,] 2.6128295 1.2076972 -0.18903700 3.6309333 -1.35852391 0.52502747
## [6,] -2.2279360 1.7791016 -1.20597457 1.0571294 -0.57339884 1.63350853
## [7,] -0.8282720 1.4754709 -0.38121196 -0.8253620 1.16853433 0.08396288
## [8,] -1.3826889 0.4116210 -1.78965910 1.3498485 0.88184490 0.56031956
## [9,] -2.4988707 -2.8057423 0.07558119 -0.2581050 0.48210929 -1.78630243
## [10,] -0.9108967 4.3691958 -0.10709005 2.5931729 -1.00821722 -0.38977567
## [11,] -2.0506795 1.1691831 -1.40869336 1.2404386 -0.61605783 1.97442209
## [12,] -1.7135684 -0.1199584 0.48353594 1.2159022 1.18212102 -0.66240489
## [13,] -1.3700543 -0.6228237 0.04649359 1.4258482 -1.00970676 1.28502390
## [14,] -2.5149806 -4.2115682 1.35684939 1.6831248 0.05273502 -0.12617619
## [15,] 2.0762965 -0.2755917 -0.76867673 1.2246233 -0.90646219 0.54479486
## [16,] -0.5116489 -1.0998629 -0.75110047 0.5155583 0.05990936 -0.26377246
## [17,] -4.7771668 4.3060377 -1.50604446 -0.2426992 0.94797294 2.61778723
## [18,] -2.3034036 -0.2044303 -2.31393112 1.5616273 -0.05644338 -0.36995731
## [19,] -0.4071411 0.7486259 -0.41520969 1.2877298 -0.28991360 1.01219584
## [20,] -1.1977801 1.0634716 -0.52625141 1.1745420 -0.83615997 -0.83545511
## [21,] -1.1890503 -1.6608322 3.49584000 1.1665141 0.73463956 -1.78704136
## [22,] -1.1512296 -1.7267131 0.38683127 -0.7195584 0.75264573 -0.65461937
## [23,] -1.0600826 -0.9489989 0.08094950 -0.8479636 1.58579695 -0.06424196
## [24,] 1.1496231 1.0198538 -1.92018938 -0.3317371 -0.53936695 0.59072867
## [25,] 0.2651844 2.4037982 0.49374343 -0.2448217 0.32016818 -0.33049077
## [26,] 1.6048797 0.2167301 0.44938604 2.4042743 -0.11351876 -0.07733417
## [27,] -0.1355331 -1.0296461 1.09688996 0.8760724 -0.29289105 -0.93654977
## [28,] 2.1461147 -4.5485150 0.02623263 0.7284910 -0.97357690 -0.13225379
## [29,] 0.9000000 -0.6013000 2.00000000 0.5000000 -0.72000000 -0.64000000
## [30,] 0.5535363 2.2079160 0.36159485 1.8782757 -0.73090825 -0.16021286
## [31,] 0.5579728 -0.5012534 3.53268415 -0.7956229 -0.14021484 -0.51037642
## [32,] -1.4025020 -0.4331542 -0.25024455 0.2950250 -0.13910644 -1.23359252
## [33,] 3.3379829 -2.3800425 0.43428288 3.0860127 -1.23075426 -1.92820430
## [34,] 1.1540912 -1.7535453 0.08302577 0.8090087 -0.49286072 -0.17190885
## [35,] -2.1114611 1.9888498 -0.64616627 0.7607303 0.39476142 0.41183028
## [36,] -1.6844748 1.2920278 1.96527333 0.4804940 0.06424053 0.66946574map_params <- find_tinkerbell(time = 120,
n_maps = 96,
plot = FALSE,
export = FALSE,
file_name = "maps")Preparing output directory
output <- "/Users/martinp4/Documents/Cedars/Oneiric/simulations/"Territory Types
Here, we demonstrate the possible territory types that can be created using oneiric.
circle <- simulate_spatial(n_cells = 5000,
n_territories = 5,
n_samples = 12,
pattern = "circle",
expanse = c(0.1, 0.3))
rod <- simulate_spatial(n_cells = 5000,
n_territories = 5,
n_samples = 12,
pattern = "rod",
width_range = c(0.0, 0.1),
length_range = c(0.2, 0.5))
chaos_map <- simulate_spatial(n_cells = 5000,
n_samples = 12,
pattern = "chaos",
expanse = 0.02)
layered_map <- simulate_spatial(n_cells = 5000,
n_samples = 12,
n_territories = 1,
pattern = "circle",
layers = 5,
expanse = c(0.4, 0.5))circles <- do.call("rbind", circle)
circles$Territory <- as.factor(circles$Territory)
cols <- colorRampPalette(RColorBrewer::brewer.pal(11, "Spectral"))
cols <- cols(length(levels(circles$Territory)))
g <- ggplot(circles, aes(x = x, y = y, col = Territory)) +
geom_point(size = 0.5) +
theme_bw() +
scale_color_manual(values = cols) +
facet_wrap(~sample) +
guides(colour = guide_legend(
override.aes = list(size = 5)))
rods <- do.call("rbind", rod)
rods$Territory <- as.factor(rods$Territory)
cols <- colorRampPalette(RColorBrewer::brewer.pal(11, "Spectral"))
cols <- cols(length(levels(rods$Territory)))
g1 <- ggplot(rods, aes(x = x, y = y, col = Territory)) +
geom_point(size = 0.5) +
theme_bw() +
scale_color_manual(values = cols) +
facet_wrap(~sample) +
guides(colour = guide_legend(
override.aes = list(size = 5)))
tinker <- do.call("rbind", chaos_map)
tinker$Territory <- as.factor(tinker$Territory)
cols <- colorRampPalette(RColorBrewer::brewer.pal(11, "Spectral"))
cols <- cols(length(levels(tinker$Territory)))
g2 <- ggplot(tinker, aes(x = x, y = y, col = Territory)) +
geom_point(size = 0.5) +
theme_bw() +
scale_color_manual(values = cols) +
facet_wrap(~sample) +
guides(colour = guide_legend(
override.aes = list(size = 5)))
layers <- do.call("rbind", layered_map)
layers$Territory <- as.factor(layers$Territory)
cols <- colorRampPalette(RColorBrewer::brewer.pal(11, "Spectral"))
cols <- cols(length(levels(layers$Territory)))
g3 <- ggplot(layers, aes(x = x, y = y, col = Territory)) +
geom_point(size = 0.5) +
theme_bw() +
scale_color_manual(values = cols) +
facet_wrap(~sample) +
guides(colour = guide_legend(
override.aes = list(size = 5)))print(g)
Circle Territories
print(g1)